Segmentation of polarimetric SAR data with a multi-texture product model

The previously proposed multi-texture model for multi-looked PolSAR data statistics [1] is hereby implemented into an advanced statistical clustering algorithm and tested on several real PolSAR images. The multi-texture model is based on the product model for SAR statistics, yet allows the possibility of different texture parameters for the co-polarized (co-pol) and cross-polarized (cross-pol) channels. The implementation automatically determines the most appropriate texture model between the proposed "dual-texture" model and the traditional "scalar-texture" model. The clustering algorithm is implemented as a multi-texture version of [2]. It incorporates the flexible U-distribution, contextual smoothing with Markov random fields, and determines the number of classes with goodness-of-fit tests. The real SAR examples indicate that multi-texture is not generally required and we discuss the possible mis-interpretation of multi-texture in alternative window-based estimation methods, due to mixing of different polarimetric classes.